{
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   "source": [
    "import gensim\n",
    "from gensim.models.doc2vec import Doc2Vec\n",
    "TaggededDocument=gensim.models.doc2vec.TaggedDocument\n",
    "def get_data():\n",
    "    with open(\"train.txt\",'r',encoding='utf-8') as f:\n",
    "        docs=f.readlines()\n",
    "    train_data=[]\n",
    "    for i ,text in enumerate(docs):\n",
    "        word_list=text.split(' ')\n",
    "        word_list[len(word_list)-1]=word_list[len(word_list)-1].strip()\n",
    "        document=TaggededDocument(word_list,tags=[i])\n",
    "        train_data.append(document)\n",
    "    return train_data\n",
    "def train_model(x_train):\n",
    "    model_dm=Doc2Vec(x_train,min_count=1,window=3,vector_size=20,negative=5,workers=4,dm=1)\n",
    "    model_dm.train(x_train,total_examples=model_dm.corpus_count,epochs=70)\n",
    "    model_dm.save(\"model_doc2vec\")\n",
    "    return model_dm\n",
    "def test():\n",
    "    model_dm=Doc2Vec.load(model_doc2vec)\n",
    "    test_text=['科学','教育','是','难搞','的']\n",
    "    inferred_vector_dm=model_dm.infer_vector(test_text)\n",
    "    sims=model_dm.docvecs.most_similar([inferred_vector_dm],topn=10)\n",
    "    return sims\n",
    "if __name__=='__main__':\n",
    "    train_data=get_data()\n",
    "    model_dm=train_model(train_data)\n",
    "    sims=test()\n",
    "    peint(\"相似文本、相似度和文本中词的数量：\")\n",
    "    for count, sim in sims:\n",
    "        sentence=train_data[count]\n",
    "        words = ''\n",
    "        for word in sentece[0]:\n",
    "            words = words + word + ' '\n",
    "        print(words,sim,len(sentence[0]))"
   ]
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  {
   "cell_type": "code",
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   "metadata": {},
   "outputs": [],
   "source": []
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